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ComMod: an identity funded on a charter
The ComMod charter is evolving. The very first version (1.0) has been
finalized on February 23th, 2003. You can download it here
(pdf format). The current version (1.1) has been finalized on April 5th,
2004. The text is presented online below. This latest version is also
available here
(pdf format).
Version 1.1 (April 5th, 2004)
The team posture
Our research activities are involved in the development process, and
addressing genuine stakes and implementing them in the field is our preferred
way to test and to question theories. We are dealing with a combination
of pragmatic and theoretical questions regarding the management of renewable
resources and the environment, and are facing complex and very dynamic
research objects. Such a context leads to pay importance to uncertainty,
and to the existence of multiple and legitimate points of view, including
the ones produced by scientific expertise. These different view points
deserve to be taken into account in an iterative process of understanding,
confrontation, and analysis. Therefore, we have chosen to give ourselves
a rigorous and refutable doctrin which could be evaluated. It means that:
- The fate of all the assumptions backing modelling work is to be discarded
after each interaction with the field, that is to say to be voluntarily
and directly subjected to refutation,
- Having no a priori implicit experimental hypothesis is an
objective implying the adoption of procedures to unveil such implicit
hypotheses,
- The impact in the field has to be taken into consideration as soon
as the first steps of the approach, in terms of research objectives,
quality of the approach, quantified monitoring and evaluation indicators.
- Particular attention should be given to the process of validation
of such a research approach, knowing that a general theory of model
validation does not exist, and that procedures differing from those
used in the case of physical, biological, and mathematical models need
to be considered.
The present first version of this document (“Charter 1.0”)
results from a long discussion, and numerous common activities among its
different signatories over the last two years. By definition, the present
charter is an evolving one and will be periodically revised and updated
with the agreement of its signatories. This is because struggling for
rigor implies its improvement at each step, and striving for refutation
leads to continuous questioning of our posture.
The common approach
In agreement with the complex and dynamic nature of the processes under
study, our companion modelling approach requires a permanent and iterative
confrontation between theories and field circumstances. Therefore, it
is based on repetitive back and forth steps between the model and the
field situation. Thus, this approach is adapted to the complexity and
the openess of the systems under study because: i) it considers as legitimate
and takes into account points of view which could possibly be contradictory,
ii) it organises the compulsory questioning of any new element introduced
in the approach, iii) during each loop, it confronts itself to new external
elements.
Because in the field of cognitive sciences Multi-Agent Systems (MAS) are
particularly adapted to the exploration of hypotheses presented as “true”,
and to the representation of dynamic and complex systems (Janssen 2002),
this simulation tool has been privileged in our approach and is used in
association with other ones.
The importance given to field work in our approach leads to expectations
regarding tangible effects at these sites. Depending on the experiences
and tools used, the outputs could be of three kinds: the modification
of perceptions, of behaviours, or of actions. Finally, one needs to distinguish
between the use of this approach in two specific contexts: the production
of knowledge on some complex systems, and the support to collective decision-making
processes. While the first context corresponds to systems research via
a particular relationship to field work, the second one corresponds to
methodological research to facilitate a concerted management of such systems
by proposing a particular relationship to field work to achieve such an
end.
First objective: understanding complex environments
In this context, modelling deals with the dialectique among the researcher,
the model and the field. Simulation accompanies an iterative research
process, which is specific to each situation. The endless following cycle
“field work-> modelling -> simulation -> field work again,
etc.” corresponds to this concept. This leads to accept a diversity
of models and methods, each contributing to a new kind of relationship
between the simulation, the research itinerary, and the the decision-making
process.
The researcher starts building a first preliminary model to explicit
his/her theoretical as well as field-based pre-conceptions. The confrontation
of this first model with real circumstances leads to revise and to re-build
it, taking gradually into account the features of the field situation,
but also the questions that stakeholders are asking to themselves. The
discussion of the model hypotheses, and the simulations implemented according
to an experimental plan corresponding to the initial questions, allows
to modify the formers and to formulate new questions. This process leads
to the construction of a new model, which is either derived from the previous
one following its confrontation with the real circumstances and its evolution,
or an entirely new one. As this cycle repeates itself, we create a family
of models representing the successive interactions between the researcher
and the field. There is no gradual a priori complexification
of a model incorporating more and more elements to fit with “the
reality”.
Such a family of models is a genuine knowledge-based system allowing
interacting researchers and stakeholders to increase their personal and
common knowledge of the system, of the current processes, and of the situation
of each actor-observer in such processes (Berkes and Folke 1998). Here,
the key challenge of companion modelling is to deliver an improved understanding
of these processes rather than a “turn key” itinerary for
renewable resources management. As a consequence, there is a special relationship
between the field and the model (sensu lato). Instead of proposing
a simplification of stakeholders knowledge, the model is seeking a mutual
recognition of everyone representation of the problematique under study.
Such mutual recognition lies on indicators which are gradually and collectively
built during the implementation of the approach, and constitutes the fundamentals
of participatory modelling.
The underlying hypothesis is that, in most of the renewable resource
management situations, what actors need is less a simple formalisation
of their own perception than an exchange among stakeholders (including
experts) about such representations, and existing knowledge. By structuring
these exchanges, the simulation helps the stakeholders to validate the
interactions between different representations and the system dynamics
integrated in the model. A true learning process on the system under study
is taking place through interactions with and among local stakeholders
(Conein and Jacopin 1994).
Second objective: to support collective decision-making processes in complex
situations
In this case, the approach facilitates collective decision-making processes
by making more explicit the various points of view and subjective criteria,
to which the different stakeholders refer implicitely or even unconsciously.
Indeed, as demonstrated in past research (Mermet 1992; Weber and Reveret
1993; Ostrom, Gardner et al. 1994; Funtowicz, Ravetz et al. 1999), when
facing a complex situation, the decision-making process is evolving, iterative,
and continuous. It means that this process produces always imperfect “decision
acts”, but following each iteration they are less imperfect and
more shared. In other words, the question is not the quality of the choice,
but the quality of the process leading to it. It is not about finding
the best solution, but to take into consideration as well as possible
the uncertainties of the situation. To improve the quality of collective
decision-making processes, the approach aims at elucidating and sharing
the points of view determining them. This approach refers to a dynamic
perception of the decision-making process in which the scientific and
technical perception is only one among others, and not the pre-supposed
right perception toward which the decision should be attracted. The objective
is not to ambitiously produce decisions and definitive results, but to
enrich the decision-making process in terms of technical (information,
technical quality of actions launched, etc.), or sociological (greater
concertation, reinforcement of stakeholders power in making decisions,
etc.) aspects. Because we are dealing with an evolving, iterative, and
continuous process, the way to accompany it should also bear the same
characteristics.
What tools can participate in such a process, and how to use them to
accompany the collective decision-making dynamics? That is to say how
to help stakeholders govern a situation along a continuous and gradually
enriched itinerary, instead of proposing ready-made expert solutions?
This is a situation similar to experimental approaches in post-normal
science in which, based on a shared conception of the evolution of the
current situation, the stakeholders can be collectively engaged in a process
to take uncertainties into account (Funtowicz and Ravetz 1994). We propose
the use of various tools to accompany and to support the decison-making
process: MAS, role-playing games, geographic information systems, economic
tools, etc. Depending on the situation, the production of knowledge or
points of view on a given system could lead to: i) an improved knowledge
of actors/decision-makers, ii) a facilitated dialogue among stakeholders
(including experts) providing a framework for discussion and sharing of
information, an exchange of viewpoints, knowledge, and beliefs among them,
iii) a negotiation support system aiming at closing the gap between diverging
points of view in a given conflicting situation.
Here, even if it is not covering the whole process of mediation by itself,
companion modelling is taking part in it. Stakeholders learn collectively
by creating, modifying, and observing simulations. When carrying out simulations
one acts on the decision-making process by creating or modifying representations.
Companion modelling leads stakeholders to share representations and simulations
taking into account possible decisions and actions related to their environment
which are under consideration (management rules, new infrastructures,
etc.). Meanwhile, companion modelling does not include the other possible
steps of the mediation process dealing with a more quantified expertise
(size of a new infrastructure, estimated production, etc.). Companion
modelling intervenes upstream of the technical decision to support the
reflexion of concerned actors, in order to produce a shared representation
of the problematique, and to identify possible ways toward a process of
collective management of the problem.
A joint use
We consider that the organization of action is a result emerging from
a dynamic of interactions among individual and/or collective stakeholders.
Such a dynamic is constrained by the understanding and perception that
every actor has of others’actions, therefore of his own indicators
concerning the environment he is sharing with others. Consequently, it
is fundamental to distinguish rigorously between the two aboved-mentioned
kinds of use of our approach, even if in practice they are often implemented
simultaneously. The first kind of use looks for its scientific legitimacy
in the production and relevance of knowledge, while the second one aims
at improving the quality of collective decision-making processes.
In both cases, there is production of knowledge through the interaction
among researchers and local stakeholders. But in the first situation,
this production of knowledge (being for researchers, or for local actors
through training activities) is the objective, while in the second case
we make the hypothesis that it is a necessary element of the method to
achieve the main objective of supporting collective decisions. This distinction
is as much a methodological question than a epistemological and analytical
one: nothing can guarantee that the tools and the methods tested in a
given situation will be useful, efficient, and adapted in another one,
particularly regarding the posture of the researcher-modeller in the process.
This is why we tackle these two modelling problematics differently. On
the other hand, we think that it is necessary to consider them jointly,
because the points of view produced by each of the two modelling situations
are useful to elucidate the secondary effects created by one of them.
List of signatories
- Martine Antona (economist Cirad)
- Patrick d'Aquino (geographer Cirad)
- Sigrid Aubert (jurist Cirad)
- Olivier Barreteau (water scientist Cemagref)
- Stanislas Boissau (sociologist Cirad)
- François Bousquet (modeller Cirad)
- William's Daré (sociologist Cemagref)
- Michel Etienne (plant ecologist INRA)
- Christophe Le Page (modeller Cirad)
- Raphaël Mathevet (animal ecologist CNRS)
- Guy Trébuil (agronomist Cirad)
- Jacques Weber (economist Cirad)
Last update : November
30, 2010 |
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